scholarly journals Image diagnosis models for the oral assessment of older people using convolutional neural networks: A retrospective observational study

2021 ◽  
Author(s):  
Misato Muramatsu ◽  
Masumi Muramatsu ◽  
Naoto Takahashi ◽  
Atsuko Hagiwara ◽  
Jyun Hagiwara ◽  
...  
Keyword(s):  
Neural Networks ◽  
Observational Study ◽  
Older People ◽  
Convolutional Neural Networks ◽  
Retrospective Observational Study ◽  
Oral Assessment ◽  
Image Diagnosis

scholarly journals Prevalence, management, and outcomes of SARS-CoV-2 infections in older people and those with dementia in mental health wards in London, UK: a retrospective observational study

2020 ◽  
Vol 7 (12) ◽  
pp. 1054-1063 ◽  
Author(s):  
Gill Livingston ◽  
Hossein Rostamipour ◽  
Paul Gallagher ◽  
Chris Kalafatis ◽  
Abhishek Shastri ◽  
...  
Keyword(s):  
Mental Health ◽  
Observational Study ◽  
Older People ◽  
Retrospective Observational Study

Hemmkörperentwicklung bei Hämophilie-Patienten nach Präparate wechsel

2012 ◽  
Vol 32 (S 01) ◽  
pp. S39-S42 ◽  
Author(s):  
S. Kocher ◽  
G. Asmelash ◽  
V. Makki ◽  
S. Müller ◽  
S. Krekeler ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Observational Study ◽  
Retrospective Observational Study ◽  
Frequent Change ◽  
Patient Age ◽  
Treatment Regimens ◽  
Patient Âgé ◽  
The Relationship ◽  
Observation Period

SummaryThe retrospective observational study surveys the relationship between development of inhibitors in the treatment of haemophilia patients and risk factors such as changing FVIII products. A total of 119 patients were included in this study, 198 changes of FVIII products were evaluated. Results: During the observation period of 12 months none of the patients developed an inhibitor, which was temporally associated with a change of FVIII products. A frequent change of FVIII products didn’t lead to an increase in inhibitor risk. The change between plasmatic and recombinant preparations could not be confirmed as a risk factor. Furthermore, no correlation between treatment regimens, severity, patient age and comorbidities of the patients could be found.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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